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440
Robust Monte Carlo Localization for Mobile Robots
, 2001
"... Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), whi ..."
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Cited by 826 (88 self)
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Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm to mobile robots equipped with range finders, a kernel density tree is learned that permits fast sampling. Systematic empirical results illustrate the robustness and computational efficiency of the approach.
Unscented Filtering and Nonlinear Estimation
 PROCEEDINGS OF THE IEEE
, 2004
"... The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the ..."
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Cited by 555 (3 self)
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The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT.
A solution to the simultaneous localization and map building (SLAM) problem
 IEEE Transactions on Robotics and Automation
, 2001
"... Abstract—The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle ..."
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Cited by 492 (30 self)
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Abstract—The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from the estimationtheoretic foundations of this problem developed in [1]–[3], this paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. This paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeterwave (MMW) radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are crosscompared with absolute locations of the map landmarks obtained by surveying. In conclusion, this paper discusses a number of key issues raised by the solution to the SLAM problem including suboptimal mapbuilding algorithms and map management. Index Terms—Autonomous navigation, millimeter wave radar, simultaneous localization and map building. I.
Data Assimilation Using an Ensemble Kalman Filter Technique
, 1998
"... The possibility of performing data assimilation using the flowdependent statistics calculated from an ensemble of shortrange forecasts (a technique referred to as ensemble Kalman filtering) is examined in an idealized environment. Using a threelevel, quasigeostrophic, T21 model and simulated ob ..."
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Cited by 411 (5 self)
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The possibility of performing data assimilation using the flowdependent statistics calculated from an ensemble of shortrange forecasts (a technique referred to as ensemble Kalman filtering) is examined in an idealized environment. Using a threelevel, quasigeostrophic, T21 model and simulated observations, experiments are performed in a perfectmodel context. By using forward interpolation operators from the model state to the observations, the ensemble Kalman filter is able to utilize nonconventional observations. In order to
Parameter Estimation Techniques: A Tutorial with Application to Conic Fitting
, 1995
"... Almost all problems in computer vision are related in one form or another to the problem of estimating parameters from noisy data. In this tutorial, we present what is probably the most commonly used techniques for parameter estimation. These include linear leastsquares (pseudoinverse and eigen a ..."
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Cited by 276 (8 self)
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Almost all problems in computer vision are related in one form or another to the problem of estimating parameters from noisy data. In this tutorial, we present what is probably the most commonly used techniques for parameter estimation. These include linear leastsquares (pseudoinverse and eigen analysis); orthogonal leastsquares; gradientweighted leastsquares; biascorrected renormalization; Kalman filtering; and robust techniques (clustering, regression diagnostics, Mestimators, least median of squares). Particular attention has been devoted to discussions about the choice of appropriate minimization criteria and the robustness of the different techniques. Their application to conic fitting is described.
Optimization of the Simultaneous Localization and Map Building Algorithm for Real Time Implementation
 IEEE Transactions on Robotics and Automation
, 2001
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iSAM: Incremental Smoothing and Mapping
, 2008
"... We present incremental smoothing and mapping (iSAM), a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing informatio ..."
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Cited by 153 (35 self)
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We present incremental smoothing and mapping (iSAM), a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, therefore recalculating only the matrix entries that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fillin in the factor matrix by periodic variable reordering. Also, to enable data association in realtime, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and realworld datasets for both landmark and poseonly settings.
Optimal motion and structure estimation
 IEEE Trans. Pattern Anal. Mach. Intell
, 1993
"... This paper studies optimal estimation for motion and structure from point correspondences. (1) A study of the characteristics of thc problem provides insight into the need for optimal estimation. (2) Methods have been developed for optimal estimation with known or unknown noise distribution. The sim ..."
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Cited by 152 (5 self)
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This paper studies optimal estimation for motion and structure from point correspondences. (1) A study of the characteristics of thc problem provides insight into the need for optimal estimation. (2) Methods have been developed for optimal estimation with known or unknown noise distribution. The simulations showed that the optimal estimations achieve remarkable improvement over the preliminary estimates given by the linear algorithm. (3) An approach to estimating errors in the optimized solution is presented. (4) The performance of the algorithm is compared with a theoretical lower bound CramCrRao bound. Simulations show that the actual errors have essentially reached the bound. (5) A batch leastsquares technique (LevenbergMarquardt) and a sequential leastsquares technique (iterated extended Kalman filtering) are analyzed and compared. The analysis and experiments show that, in general, a batch technique will perform better than a sequential technique for any nonlinear problems. Recursive batch processing technique is proposed for nonlinear problems that require recursive estimation. 1.
An Efficient Method for Contour Tracking using Active Shape Models
 In Proceeding of the Workshop on Motion of Nonrigid and Articulated Objects. IEEE Computer Society
, 1994
"... There has been considerable research interest recently, in the areas of real time contour tracking and active shape models. This paper demonstrates how dynamic filtering can be used in combination with a flexible shape model to track an articulated nonrigid body in motion. The results show the meth ..."
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Cited by 137 (9 self)
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There has been considerable research interest recently, in the areas of real time contour tracking and active shape models. This paper demonstrates how dynamic filtering can be used in combination with a flexible shape model to track an articulated nonrigid body in motion. The results show the method being used to track the silhouette of a walking pedestrian across a scene in real time. The active shape model used was generated automatically from real image data and incorporates variability in shape due to orientation as well as object flexibility. A Kalman filter is used to control spatial scale for feature search over successive frames and for contour refinement on an individual frame. Iterative refinement allows accurate contour localisation where feasible, although there is a tradeoff between speed and accuracy. The shape model incorporates knowledge of the likely shape of the contour and speeds up tracking by reducing the number of system parameters. A further increase in speed ...
Faugeras O. D.: Selfcalibration of a moving camera from point correspondences and fundamental matrices
 International Journal of Computer Vision
, 1997
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